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1 Spatial variability in distribution of organic carbon stocks in the soils of North East India B. U. Choudhury 1, *, K. P. Mohapatra 1, Anup Das 1, Pratibha T. Das 2, L. Nongkhlaw 1, R. Abdul Fiyaz 1, S. V. Ngachan 1, S. Hazarika 1, D. J. Rajkhowa 1 and G. C. Munda 1 1 ICAR Research Complex for NEH Region, Umaim , India 2 Northeastern Space Application Centre, Barapani , India Northeastern region (NER) of India has wide variation in physiography and climatic conditions. Because of its strategic settings in the phyto-biomass-rich landscape of the Eastern Himalaya, the soils are rich in organic carbon. However, sporadic information on field-scale observations is available on soil organic carbon (SOC) content at regional level. Information on status and spatial variability of SOC and its complex interaction with land-use systems is scanty. Therefore, an attempt was made to estimate spatial variability in SOC inventories for surface soils across six states of NER (viz. Assam, Manipur, Meghalaya, Nagaland, Sikkim and Tripura covering a geographical area of m ha) in Geographical Information System (GIS) environment. Results revealed that the soils were very high in SOC content 98.54% area had >1% and 14.4% area had > 2.5% SOC content. Similarly, 76.5% area had SOC density of Mg/ha and 8% area had very high SOC density of Mg/ha. A total of Tg (1 Tg = g) SOC stocks was estimated on an area of m ha surface soils representing all major land-use systems, with a major share (>50%) coming from forest soils. Complex interaction of geographic location, rainfall, soil texture and land-use practices significantly influenced spatial variation in SOC content, density and stock. The SOC content as percentage of total geographical area was highest in Sikkim followed by Nagaland, Manipur, Meghalaya, Assam and Tripura. Keywords: Land-use systems, phyto-biomass, soil organic carbon, spatial variability. *For correspondence. ( 604 SOIL organic carbon (SOC) is one of the most important indicators of soil fertility, productivity and quality. Decline in SOC creates an array of negative effects on land productivity 1 3. Hence maintaining and improving its level is a pre-requisite to ensure soil quality, crop productivity and sustainability of agricultural ecosystems 4. Since soil contains a significant part of the global carbon stock (3.5%) 5, there is growing interest in assessing the role of soil as a sink for carbon under different land-use management practices, including forest ecosystems 1,2,6 8. Increase in SOC content by 0.01% can substantially reduce the adverse consequences of annual increase in atmospheric carbon dioxide concentration 9. The Northeastern region (NER) of India, by virtue of its geographic location and richness in biodiversity, well supported by complementary climatic factors, more particularly high rainfall for luxuriant phyto-biomass (both above and below ground) in the form of forests and allied sources of vegetation, has a unique place in the country 10. The region experiences hot summer and cold winter. The temperature varies from as low as 0 C in the Himalayan range (Sikkim) to 35 C in some parts of Tripura. The region falls under high rainfall areas, with Cherapunjee Plateau (Meghalaya) receiving over 11,000 mm during monsoon season (June September). The region is characterized by steep to very steep slopes in Meghalaya, Manipur, Nagaland and Sikkim, and gentle slopes in the Assam valley. The topography, climate and vegetation have played a great role in the formation of a variety of soils in the region. These could be broadly put under Red and Yellow, Brown Hill, Old and Recent Alluvial and Terai soils 11. Phyto-biomass is one of the most important renewable sources that enriches the soil with organic carbon. However, expanding crop lands to meet the food requirement always comes at the cost of reduced carbon stocks in natural vegetation and soils 2,3 ; similar is the situation in NER. There is burning of phyto-biomass of more than 8.5 million tonnes (mt) annually at the rate of 10 Mg/ha from shifting cultivation alone 12. The magnitude of variation in SOC content and stock (increase/decrease) depends on the type of land-use, degree of land-use change and post-conversion land management 2,13. Since the NER is highly variable in climatic conditions (tropical to alpine climate), topography, rainfall pattern, vegetation, land use and cultural diversity and ethnicity, the SOC content is expected to be highly variable across the entire region. However, limited fieldscale observations are available on the spatial distribution of SOC content at the regional level 14,15. Lack of any spatial information on land-use pattern vis-à-vis soil fertility indicators like SOC of NER makes it difficult to

2 Figure 1. Study area representing geographical location, including relative elevation (m). implement any large-scale or location-specific sustainable agricultural development programme. With the advent of geospatial tools like satellite remote sensing and geographic information system (GIS), it is now possible to integrate them with traditional pointbased observations to generate land-use land-cover map from multi-temporal data and spatial databases on natural resources, including soil carbon inventories at the regional scale. The outcome of the findings would give a new dimension at the regional level land-use planning, agricultural area diversification and future projection studies. It would also be helpful in devising location-specific mitigation and adaptation strategies related to carbon sequestration and climate-resilient agriculture. Therefore, an attempt was made to develop the SOC map of six states of NER with the help of point data from field experimentation and secondary sources and study the SOC variability pattern with respect to land use, physiography and climate. Materials and methods Study area The study area includes six states of NER, viz. Assam, Meghalaya, Manipur, Nagaland, Tripura and Sikkim having a geographical area (GA) of m ha (nearly 4.8% area of the country; Figure 1) with a population of more than 43 million 16. About 35% of the area in the region is occupied by the plains, except Assam where it is about 84.4% of GA. The region has a distinct land-use pattern as compared to the rest of the country. Nearly 15.3% of GA is under cultivation; forest cover in the region varies from 35.28% (Assam) to 90.68% (Mizoram) 17. Area under shifting cultivation is 3.36% (0.877 m ha) 18. The land-use pattern and distribution in the six states of NER is presented in Table 1 and Figure 2. Data source For collection of soil samples, a grid of 15 km 15 km was prepared in ArcGIS and then overlaid on the land-use map of the study area (Figure 2) derived from multitemporal LISS-III satellite image (23.5 m spatial resolution) of Indian Remote Sensing Satellite (IRPS-P6) 19. A total of 694 grids, each with an area of 225 km 2 (15 km 15 km) covered the total geographical area (TGA) of the study area (15.61 m ha). Out of 694 grids, 457 grids mostly representing major land-use systems spread across the six studied NE states (10.1 m ha areas) were selected for collection of soil samples. From each of these grids, geo-referenced composite surface (0 15 cm) soil samples were collected. Data were also collected from 285 additional geo-referenced locations of the six states studied 605

3 Table 1. Land-use land-cover statistics of NER derived from multi-temporal remote sensing data of IRS-P6-LISS-III sensor ( ) 19 State Assam Manipur Meghalaya Nagaland Sikkim Tripura Total Geographical area (m ha) Land use (% of geographical area) Built-up Crop land Dense forest Open forest Plantation crops Shifting cultivation* Snow-covered area Grassland Wasteland Water bodies *Current + abandoned; Source: Ref. 19. Figure 2. Land-use land-cover map of NER derived from LISS-III ( ) data 19. on surface soil texture (sand, silt and clay contents) and SOC status of surface layer from different secondary sources like published soil database generated through soil resource mapping of India by the National Bureau of Soil Survey and Land Use Planning (NBSS and LUP), Nagpur 20 24, peer-reviewed research articles and reports published by research institutions located in NER 12,14,15,25. All together (including secondary data on 285 locations), 742 observations (sample size) representing major land uses, namely forests dense and open (254), agriculture (crop land 168), shifting cultivation (65), plantation 606 and horticultural crops (87), grassland (75) and wasteland (93) were considered for spatial mapping of SOC inventories of NER. State-wise sample numbers representing the major land uses are presented in Table 2. The collected 457 soil samples were air-dried, crushed and ground to pass through a 0.5 mm sieve and then analysed for SOC following wet digestion method 26. Soils are mostly acidic in reaction and acid soils below ph 5.5 occupy more than 60% of the studied area 11. Presence of toxic concentrations of Al 3+ and to a lesser extent Mn 2+, deficiency of bases (Ca, Mg, K) due to extensive

4 leaching and their poor retention power in clay complex, high P-fixation capacity of soil caused by highly active Al 3+ and Fe 3+ surfaces are some of the major constraints in achieving optimum crop productivity 11,27. Soil textural class was determined by hydrometer method 28. Average percentage of sand content varied from 26.9 in Nagaland to 47.3 in Meghalaya, whereas silt content (%) varied from 28.4 in Tripura to 40.6 in Sikkim. Similarly, clay content (%) varied from 18.8 in Sikkim to 32.6 in Nagaland (Table 2). Model-estimated average bulk density (BD) values computed from soil texture (silt and clay) and SOC-dependent pedo transfer function (PTF) developed by Kaur et al. 29 reflected a variation from as low as 0.88 (±0.16) in Sikkim to as high as 1.31 g/cm 3 (±0.18) in Tripura (Table 2). Computation of SOC stock Several PTFs have been developed for estimation of BD based on SOC only 30,31, whereas few are based on both SOC and textural data 32,33. We estimated soil BD (Table 2) using PTF developed by Kaur et al. 29, which was based on both SOC and soil texture. The algorithm is as follows BD (g/cm 3 ) = exp{ (% SOC) (% clay) (% silt)}. SOC densities were estimated from the SOC contents, estimated BD values and the corresponding soil depth by using the following equation SOC density (Mg/ha) = SOC (g/g) BD (g/cm 3 ) soil depth (cm). For estimation of SOC stocks under various land-use systems in each state, average SOC density values (Mg/ha) estimated by Choudhury et al. 34 under major land-use systems of NER were used. Remote sensing-based area (ha) from satellite data of LISS-III sensor 19 estimated under major land-use systems (Table 1) and the corresponding SOC density of each land-use system were used Table 2. Descriptive statistics about baseline and estimated soil properties across the study area No. of Sand Silt Clay BD* State samples (%) (%) (%) (g/cm 3 ) Assam ± 0.19 Manipur ± 0.20 Meghalaya ± 0.19 Nagaland ± 0.22 Sikkim ± 0.16 Tripura ± 0.18 *Estimated BD values. to calculate SOC stocks in Tg (1 Tg = g = 10 3 Mg) by the following equation SOC stock (Tg) = SOC density (Mg/ha) area under each land use (ha) For estimation of SOC stocks, only major land-use systems representing agriculture (settled and shifting cultivation), forests (dense and open), plantation and horticulture, grassland and wasteland covering m ha of the total area of m ha area were considered. Builtup area and water bodies across the six NE states and snow-covered area of Sikkim were not included in stock estimation. To prepare spatial inventory of SOC, a point layer on SOC observation was generated in GIS environment (ArcGIS 9.3 software) using data on 742 geo-referenced sampling locations. By interpolation of this point layer using kriging, the SOC map was generated and classified into five groups representing SOC content (%), namely , , , and The state-wise area statistics was also generated from the SOC map. Similarly, spatial map of SOC density (Mg/ha) was prepared having six classes of SOC density (Mg/ha), namely 10 20, 20 30, 30 40, 40 50, and and the corresponding area in each state under the six classes was also calculated. Statistical analysis Statistical analysis was performed with SAS Version 9.2 (SAS Institute Inc., Cary, NC, USA). The PROC GLM procedure of SAS was used to conduct analysis of variance (ANOVA) to determine the statistical significance of treatment (six studied NE states) effects on SOC content and density across the study area. Each state was considered as one treatment and means of treatments (6) were compared with Duncan s Multiple Range Test (DMRT) at 0.05 probability level of significance. Statistical significant differences in SOC content and SOC density among the six studied states were also graphically represented by a diffogram using PROC GLM SAS (SAS Institute, Version 9.2). Results GIS mapping of spatial distribution of SOC content, density and stock SOC content: Spatial distribution of SOC content at the surface layer covering both arable and non-arable lands varied considerably across the study area (Figure 3). Of the TGA of m ha covering the six states of NER, 98.54% of the area had SOC content of more than 1%, 607

5 Figure 3. Spatial distribution map of SOC content across NER. Table 3. Area under different classes of SOC (%) content in NER SOC content (%) State Geographical area (m ha) Assam (1.74%)* (62.83%) (34.59%) (0.84%) 0 Manipur (2.93%) (71.63%) (21.80%) (3.64%) Meghalaya (0.07%) (19.90%) (62.86%) (15.39%) 0.04 (1.78%) Nagaland (0.81%) (4.18%) (61.56%) (33.32%) (0.14%) Sikkim (1.70%) (93.88%) (4.42%) Tripura (7.55%) (89.03%) (3.42%) 0 0 Total (1.46%) (40.85%) (43.25%) (13.44%) (1.00%) *Figures in parenthesis are values in percentage of total geographical area in the respective states. whereas 57.68% of the area had SOC content more than 1.5% (Table 3). Similarly, 43.25% of the area had SOC content %, and this class was dominant in three states, namely Manipur (71.63% of TGA), Meghalaya (62.86% of TGA) and Nagaland (61.56% of TGA). Very high organic carbon content ( %) was recorded in small areas in Sikkim (4.42%), Manipur (3.64%), Meghalaya (1.78%) and Nagaland (0.14%). In Sikkim, most of the area (>93% of TGA) had SOC content of %, whereas in Nagaland and Manipur, it was 33.33% and 21.8% respectively. In the % range, the highest percentage of area was estimated for Manipur (71.63) followed by Meghalaya (62.86) and Nagaland (61.56). Among the NE states, Tripura had highest area (>89%) 608 under relatively low SOC content ( %) followed by Assam (62.83%) and Meghalaya (19.9%). SOC content of <1% was recorded in small areas of Tripura (7.55%), Assam (1.74%) and Nagaland (0.81%; Table 3). On the basis of percentage, SOC content ( 1.5%) was highest in Sikkim (100% area) followed by Manipur (97.07%), Nagaland (95.02%), Meghalaya (80.03%), Assam (34.59%) and Tripura (3.42%; Table 3). SOC density: The SOC density of surface layer was estimated from the soil depth, BD and the corresponding SOC content for the six states of NER. On spatial interpolation (using kriging in GIS environment), wide variability among and within each state was observed

6 Figure 4. Spatial distribution map of SOC density across NER. Table 4. Spatial distribution of different classes of SOC density across NER SOC density (Mg/ha) in percentage of total geographical area State Geographical area (m ha) Assam Nil Manipur Nil Nil Meghalaya Nagaland Nil Nil Sikkim Nil Nil Nil Tripura Nil Nil Nil Nil Total (Figure 4). Average SOC density varied from 10 to 60 Mg/ha, with more than half of the area having SOC density of Mg/ha (Table 4). Nearly 24.30% of the area had SOC density of Mg/ha and only 8.0% of the area recorded SOC density of Mg/ha. Among the six states, Sikkim had maximum area (81.98%) under high SOC density (40 50 Mg/ha) followed by Manipur (13.03%) and Nagaland (4.06%). Similarly, Sikkim was the only state having considerable area (>10%) under very high SOC density (50 60 Mg/ha). In Nagaland, 57.5% of the area was under SOC density of Mg/ha and 40.4% of the area was in the range Mg/ha. SOC density in nearly half of the area in Meghalaya (49.51%) was Mg/ha, while in 42.56% of the area, SOC density was Mg/ha. Similarly, in Manipur, 49.39% of the area registered Mg/ha and in 36.5% of the area, SOC density was Mg/ha. Among the NE states, Tripura recorded highest percentage of area (81.8) under relatively low SOC density (10 20 Mg/ha). Assam also registered one-fifth of its TGA under low SOC density (10 20 Mg/ha), while in twothirds of the area (65.3%), SOC density was Mg/ha (Table 4). SOC stock: Of the TGA of m ha covering the six NE states, surface SOC stocks were estimated for 64.71% of the area (10.10 m ha; Table 5) representing only major land-use systems, excluding the area under settlements (rural and urban), water bodies and snow-covered areas of Sikkim. As percentage of area under settlement and water 609

7 Table 5. SOC stock (Tg) under major land-use systems in NER Total SOC stock (Tg) State % of GA a Crop land Dense forest Open forest PC b SC c Wasteland Grassland Total stock Assam Manipur Meghalaya Nagaland Sikkim Tripura Total SOC density d (mg/ha) a Percentage of geographical area considered for stock estimation; b PC, Plantation crops; c SC, Shifting cultivation; d Source: Choudhury et al. 34. Figure 5. Spatial distribution of (a) SOC content (%) and (b) SOC density (Mg/ha) in 3D views (in XY, YZ and ZX planes) across NER. Height of grey colour sticks represents variation in absolute amount of SOC content and density whereas pink and green colour dots indicate spatial distribution of input data of SOC content and density from west to east (Sikkim to Nagaland) and south to north (Assam to Tripura). bodies was lowest in Tripura and highest in Meghalaya, GA considered for SOC stock estimation was highest in Tripura (87.9%) and lowest in Meghalaya (52.84%). Total SOC stock of Tg was estimated from an area of 10.1 m ha surface soils of the study area in NER (Table 5). Trend analysis of SOC content and its representation in the XY, YZ and ZX planes using geo-statistical tools in ArcGIS software also reflected a distinct pattern in the distribution of SOC content and density across the NE region (Figure 5). Height of grey colour sticks indicates variation in SOC content and the estimated SOC density (without any extrapolation), while pink colour dots indicate the distribution from west (Sikkim) to east (Nagaland) and north (Assam) to south (Tripura). As we go from west (Sikkim) to east (Manipur and Nagaland) via Assam and Meghalaya, height of the sticks decreases gradually and then increases. The same has also been reflected by green dots in the ZX plane: SOC content and density decrease from Sikkim (West) to Meghalaya, Assam, Tripura and then again increase towards Manipur and Nagaland (East). Similarly, when we move from north (Sikkim) to south direction (Tripura) via Meghalaya 610 and Assam, height of the sticks again decreases (Figure 5), indicating the decreasing trend in SOC content and carbon density. Considering TGA of each state, diffogram studies also reflected significant differences in average SOC content and density among the six NE states (Figure 6). Sikkim registered significantly higher average SOC content (2.99%) and density (42.33 Mg/ha) compared to other states. Nagaland, Manipur and Meghalaya recorded statistically comparable (non-significant) state-level average SOC content ( %) and density ( Mg/ha). Assam and Tripura recorded significantly lower average SOC content ( %) and density ( Mg/ha) compared to the other four NE states. Discussion Among the many reasons for the variation in SOC density across NER (including within each state), deviation in SOC content seems to be the foremost factor since it reflected a strong positive correlation (r = to +0.79) with SOC density. Variation in SOC content is controlled

8 Figure 6. Diffograms showing significant differences among the six NE states of India in (a) SOC content and (b) SOC density. Faint horizontal and vertical lines represent the six states of NER. The bold lines passing through the squares diagonally represent non-significant or significant differences between the two corresponding states. Figure 7. Mean altitudinal, annual rainfall and SOC content variation of sampling site across the six NE states of India. Symbols are treatment means and bars are standard deviations. Common letters (a d) represent means that are statistically non-significant at 5% level of significance. by land-use change and post-management practices that play a major role 1 3,7,13. The significant influence of landuse practices on surface SOC content across NER was also recorded by Choudhury et al. 34, who reported that surface soils under grassland and dense forests contain very high SOC (>2.5%) compared to settled agriculture, including lowland paddy ( %) and shifting cultivation (1.70%). Therefore, significant variation in area under major land use and land cover in NER might be one of the reasons for considerable spatial variation in SOC content (Tables 2 5 and Figure 6). Another important observation made in the present study is the strong positive influence of altitudinal variation on SOC content (r = ). With the increase in elevation of sampling sites from mean sea level, SOC content also consistently increased (Figures 5 and 7). This was even reflected in the state average altitude versus SOC content: sampling location in Sikkim had highest mean altitude (1835 m) followed by Nagaland (1143 m), Manipur (888 m) and Meghalaya (503 m), whereas Tripura and Assam were at the lowest altitude of m (Figure 7). Distribution of SOC content also followed a similar trend, i.e. highest in 611

9 Figure 8. Per cent contribution by the NE states to the total area of estimation and total SOC stocks of the region. Sikkim followed by Nagaland, Manipur and Meghalaya and lowest in Assam and Tripura (Figure 7). With the increase in altitude from mean sea level, the temperature decreases at the rate of 1 C for every 166 m increase in altitude 35. Sikkim is located at a higher altitude (Figures 1 and 7), therefore, low temperature might have favoured the accumulation of higher SOC content. Along with altitudinal variation, temperature of a place depends on its latitude. Generally, temperature of places away from the equator (on higher latitudes) decreases with their distances from the equator 34. Sikkim (northern most part of NER) is situated in the higher latitudes ( N to N), which is 4 N to Tripura, the southernmost state of NER, which is located at lower latitude ( N to N). Due to geographical location in higher latitudes and altitudes (Figures 1 and 5), average temperature in Sikkim during both winter and summer is substantially low (6.5 C and 20 C respectively) compared to Tripura (20 C and 29.5 C) 36. Variation in temperature might have influenced SOC content, mostly due to secondary effect on SOC oxidation, decomposition and mineralization/accumulation rates. This might be one of the reasons for higher SOC content (2.99%) in Sikkim compared to considerably low SOC content (1.23%) in Tripura (Figure 7). In the present study, annual rainfall reflected a weak positive correlation (r = +0.15) with SOC content and did not depict any definite trend in the state-level annual average rainfall with SOC content across the six states of NER (Figure 7). This might be partly due to variation in spatio-temporal distribution within each state of NER rather than the total annual average rainfall only. Another possible reason could be due to the occurrence of higher amount of annual rainfall in all the six states. Even the lowest amount (1540 mm) of rainfall received in Nagaland is sufficient to support most water-intensive vegetation, including agricultural crops. This might have 612 nullified the effect of rainfall on phyto-biomass production and thus the SOC inventories. Differences in total SOC stocks among the six states were mostly due to the variation in GA considered for stock estimation (Table 5). Assam alone contributed more than half (51%) of the area followed by Manipur (14.38%), Meghalaya (11.71%), Nagaland (9.74%), Tripura (8.85%) and Sikkim (4.24%; Table 5). Similarly, Assam alone contributed 50.8% of the total SOC stocks followed by Manipur (14.31%), Meghalaya (11.42%) and Nagaland (9.56%). Tripura and Sikkim were the lowest contributors and were the only two states with higher per cent contribution of SOC stocks per unit area: 9.52% stocks against 8.85% area and 4.51% stocks against 4.24% area respectively (Figure 8). This might be due to the presence of reasonable amount of GA under the two most effective land-use systems, viz. dense forest and grassland which have the potential to sequester soil carbon from 38.8 to 47.0 Mg/ha (Table 5 and Figure 9) 34. In the study area, dense forest was dominant in per cent contribution to TGA (35.78% of m ha) and SOC stocks (41.30% of Tg; Table 5 and Figure 9). This was followed by crop land/agriculture (low and uplands) which contributed 28.9% SOC stock against an area of 31.7%. Among the remaining land uses, open forest contributed 8.82% of total SOC stocks, while shifting cultivation contributed marginal SOC stocks (3.14%) to the total amount of Tg. Among the land uses, forests (dense and open) and grassland contributed relatively higher amount of SOC stocks against their respective per cent share in total acreage: 50.14% stock against an area of 44.19% and 4.34% stock against an area of 3.10% of m ha (Figure 9). Several studies across the world affirmed the significant role of land-use and land-cover changes on soil carbon inventories 1 3,6 8,13. In the study area, surface SOC density varied widely among different land-use systems from as low as 23 Mg/ha in wasteland to

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